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    Bayesian Parameter Estimation via Filtering and Functional Approximations

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    1611.09293v1.pdf
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    1.130Mb
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    Description:
    Preprint
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    Type
    Preprint
    Authors
    Matthies, Hermann G.
    Litvinenko, Alexander cc
    Rosic, Bojana V.
    Zander, Elmar
    KAUST Department
    Extreme Computing Research Center
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2016-11-25
    Permanent link to this record
    http://hdl.handle.net/10754/626468
    
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    Abstract
    The inverse problem of determining parameters in a model by comparing some output of the model with observations is addressed. This is a description for what hat to be done to use the Gauss-Markov-Kalman filter for the Bayesian estimation and updating of parameters in a computational model. This is a filter acting on random variables, and while its Monte Carlo variant --- the Ensemble Kalman Filter (EnKF) --- is fairly straightforward, we subsequently only sketch its implementation with the help of functional representations.
    Publisher
    arXiv
    arXiv
    1611.09293
    Additional Links
    http://arxiv.org/abs/1611.09293v1
    http://arxiv.org/pdf/1611.09293v1
    Collections
    Preprints; Extreme Computing Research Center; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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